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A Highly Efficient Intrusion Detection Method Based on Hierarchical Extreme Learning Machine

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Proceedings of ELM-2017 (ELM 2017)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 10))

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Abstract

Cyber security is becoming more and more concerned by people nowadays. Intrusion detection systems (IDSs) is a major approach to ensure the confidentiality, integrity and availability of network system resources. There are many machine learning techniques applied to IDSs. In this paper, we propose a novel and rapid technique based on Hierarchical Extreme Learning Machine (H-ELM) for intrusion detection. We use NSL-KDD 2009 dataset to evaluate our method. Comparing our method with other widely used machine learning methods such as k-Nearest Neighbor (k-NN), Random Forest (RF) and Extreme Learning Machine (ELM), the experimental results show that H-ELM can perform better than or similar to other methods in overall accuracy of 72.87%, while only spends a total time of 2.04 s which is much faster than other methods.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61772561) and the Key Research & Development Plan of Hunan Province (Grant No. 2018NK2012).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, Hunan, People’s Republic of China

    Linyuan Yu, Yan Liu, Wentao Zhao & Qiang Liu

  2. College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China

    Jiaohua Qin

Authors
  1. Linyuan Yu
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  2. Yan Liu
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  3. Wentao Zhao
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  4. Qiang Liu
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  5. Jiaohua Qin
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Corresponding author

Correspondence to Linyuan Yu .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Macau, China

    Chi Man Vong

  3. Nokia Bell Labs, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics, College of Technology at the University of Houston, Houston, TX, USA

    Amaury Lendasse

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Yu, L., Liu, Y., Zhao, W., Liu, Q., Qin, J. (2019). A Highly Efficient Intrusion Detection Method Based on Hierarchical Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_29

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